Setting different seeds is not a statistically sound way to get independent (non-correlated) random numbers (with any single random number generator). You would be better off selecting different sub-sequences of a single sequence, and most random number libraries will allow you to do that, including cuRAND.
Check out the examples in the CUDA SDK, for example the EstimatePiP or EstimatePiInlineP examples use cuRAND to generate pseudo-random numbers.
For the second part of your question, as mentioned in the cuRAND manual the curand_normal()
routines return Normally distributed numbers with mean 0.0 and standard deviation 1.0 (i.e. Standard Normal Distribution). Clearly that means that you will have ~50% negative numbers.
It doesn't make sense to specify a fixed range along with the Normal distribution. You either want some other distribution (e.g. Uniform) with the fixed range or else you want the Normal distribution with a specific mean and standard distribution. To get from the Standard Normal to your target mean/std.dev. you simply multiply the random draw by the target standard deviation and add the target mean.